Systems and methods for comparing legal clauses

ABSTRACT

A system for performing one or more steps of a method is disclosed. The method includes receiving a first legal clause, generating, using a segmentation algorithm, a first hidden Markov chain comprising a plurality of first nodes based on the first legal clause, each of the plurality of first nodes corresponding to an element of the first legal clause, generating, using the segmentation algorithm, a second hidden Markov chain comprising a plurality of second nodes based on the second legal clause, each of the plurality of second nodes corresponding to an element of the second legal clause, comparing each of the plurality of first nodes with each of the plurality of second nodes to identify a difference for each of the plurality of first nodes, determine, based on the comparison, whether the difference for each of the plurality of first nodes exceeds a predetermined difference threshold.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, U.S. patent application Ser. No. 16/705,390, filed Dec.6, 2019, which is a continuation of U.S. patent application Ser. No.16/382,086, filed Apr. 11, 2019, now U.S. Pat. No. 10,565,445, theentire contents of each of which are fully incorporated herein byreference.

FIELD OF INVENTION

The present disclosure relates to systems and methods for comparinglegal clauses, and more particularly to systems and methods that comparelegal clauses (e.g., patent claims) using a deep learning model, aneural network (NN), fuzzy matching, or Levenshtein distance anddetermine whether the legal clauses are different based on thecomparison.

BACKGROUND

Legal documents tend to be difficult to read, understand, and compare,often due to the presence archaic jargon or “legalese.” As a result, itcan be hard for involved parties to understand and compare theimplications of various terms or clauses included in their documents oragreements. The analysis is even further complicated by the fact thatspecific legal terms or clauses could have different implicationsdepending on the location (e.g., jurisdiction) in which they are used.Even for those who can understand complex legal documents, analyzing andcomparing the documents can take considerable time and, in turn,expense.

Accordingly, there is a need for systems and methods for providing acomparison of two legal clauses in an efficient way. Embodiments of thepresent disclosure are directed to this and other considerations.

SUMMARY

Disclosed embodiments provide systems and methods for comparing legalclauses.

Consistent with the disclosed embodiments, various methods and systemsare disclosed. In an embodiment, a system performing a method forcomparing legal clauses is disclosed. The method may include receiving afirst legal clause. The method may include generating a first hiddenMarkov chain including a plurality of first nodes based on the firstlegal clause with each of the plurality of first nodes corresponding toan element of the first legal clause. The method may include receiving asecond legal clause. The method may include generating a second hiddenMarkov chain including a plurality of second nodes based on the secondlegal cause with each of the plurality of second nodes corresponding toan element of the second legal clause. The method may include comparingeach of the plurality of first nodes with each of the plurality ofsecond nodes to identify a difference for each of the plurality of firstnodes. The method may include determining whether the difference foreach of the plurality of nodes exceeds a predetermined differencethreshold. The method may include displaying text of the first legalclause in a first color when the difference exceeds the predeterminedminimum difference threshold with the first color differing from adefault color. The method may include displaying text of the first legalclause in a second color when the difference does not exceed thepredetermined minimum difference threshold. The second color may bedifferent from a default color and a first color.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated be like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and which are incorporated into andconstitute a portion of this disclosure, illustrate variousimplementations and aspects of the disclosed technology and, togetherwith the description, serve to explain the principles of the disclosedtechnology. In the drawings:

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure;

FIG. 2 is a component diagram of a service provider terminal accordingto an example embodiment;

FIG. 3 is a component diagram of a computing device according to anexample embodiment; and

FIG. 4A is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 4B is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 5A is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 5B is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 6A is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 6B is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

FIG. 7 is a flowchart of a method for comparing legal clauses accordingto an example embodiment.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology may, however, be embodied in many different forms and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods. Such other components not described herein may include, but arenot limited to, for example, components developed after development ofthe disclosed technology.

It is also to be understood that the mention of one or more method stepsdoes not preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified. Similarly, it isalso to be understood that the mention of one or more components in adevice or system does not preclude the presence of additional componentsor intervening components between those components expressly identified.

As used herein, the term “legalese” refers to the specialized languageof the legal profession. The goal of this disclosure is to translatelegalese to plain English.

This disclosure discusses a neural network (NN) to translate from thenormalized legal clause to a logical rule set. It is envisioned that theNN could be a recurrent neural network (RNN), a convolutional neuralnetwork (CNN), a recurrent convolutional neural network (RCNN), or adeep learning neural network.

Reference will now be made in detail to example embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure. Thecomponents and arrangements shown in FIG. 1 are not intended to limitthe disclosed embodiments as the components used to implement thedisclosed processes and features may vary.

In accordance with disclosed embodiments, system 100 may include aservice provider system 110 in communication with a computing device 120via network 105. In some embodiments, service provider system 110 mayalso be in communication with various databases. Computing device 120may be a mobile computing device (e.g., a smart phone, tablet computer,smart wearable device, portable laptop computer, voice command device,wearable augmented reality device, or other mobile computing device) ora stationary device (e.g., desktop computer).

Network 105 may be of any suitable type, including individualconnections via the internet such as cellular or WiFi networks. In someembodiments, network 105 may connect terminals using direct connectionssuch as radio-frequency identification (RFID), near-field communication(NFC), Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols, USB, or LAN. Because theinformation transmitted may be personal or confidential, securityconcerns may dictate one or more of these types of connections beencrypted or otherwise secured. In some embodiments, however, theinformation being transmitted may be less personal, and therefore thenetwork connections may be selected for convenience over security.

An example embodiment of service provider system 110 is shown in moredetail in FIG. 2. Computing device 120 may have a similar structure andcomponents that are similar to those described with respect to serviceprovider system 110. As shown, service provider system 110 may include aprocessor 210, an input/output (“I/O”) device 220, a memory 230containing an operating system (“OS”) 240 and a program 250. Forexample, service provider system 110 may be a single server or may beconfigured as a distributed computer system including multiple serversor computers that interoperate to perform one or more of the processesand functionalities associated with the disclosed embodiments. In someembodiments, service provider system 110 may further include aperipheral interface, a transceiver, a mobile network interface incommunication with processor 210, a bus configured to facilitatecommunication between the various components of the service providersystem 110, and a power source configured to power one or morecomponents of service provider system 110.

A peripheral interface may include the hardware, firmware and/orsoftware that enables communication with various peripheral devices,such as media drives (e.g., magnetic disk, solid state, or optical diskdrives), other processing devices, or any other input source used inconnection with the instant techniques. In some embodiments, aperipheral interface may include a serial port, a parallel port, ageneral-purpose input and output (GPIO) port, a game port, a universalserial bus (USB), a micro-USB port, a high definition multimedia (HDMI)port, a video port, an audio port, a Bluetooth™ port, a near-fieldcommunication (NFC) port, another like communication interface, or anycombination thereof.

In some embodiments, a transceiver may be configured to communicate withcompatible devices and ID tags when they are within a predeterminedrange. A transceiver may be compatible with one or more of:radio-frequency identification (RFID), near-field communication (NFC),Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols or similar technologies.

A mobile network interface may provide access to a cellular network, theInternet, or another wide-area network. In some embodiments, a mobilenetwork interface may include hardware, firmware, and/or software thatallows processor(s) 210 to communicate with other devices via wired orwireless networks, whether local or wide area, private or public, asknown in the art. A power source may be configured to provide anappropriate alternating current (AC) or direct current (DC) to powercomponents.

As described above, service provider system 110 may configured toremotely communicate with one or more other devices, such as computerdevice 120. According to some embodiments, service provider system 110may utilize a NN, word embeddings, a Markov chain, or a probabilisticparser to translate one or more legal clauses from legalese to plainEnglish.

Processor 210 may include one or more of a microprocessor,microcontroller, digital signal processor, co-processor or the like orcombinations thereof capable of executing stored instructions andoperating upon stored data. Memory 230 may include, in someimplementations, one or more suitable types of memory (e.g. such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic disks, optical disks,floppy disks, hard disks, removable cartridges, flash memory, aredundant array of independent disks (RAID), and the like), for storingfiles including an operating system, application programs (including,for example, a web browser application, a widget or gadget engine, andor other applications, as necessary), executable instructions and data.In one embodiment, the processing techniques described herein areimplemented as a combination of executable instructions and data withinthe memory 230.

Processor 210 may be one or more known processing devices, such as amicroprocessor from the Pentium™ family manufactured by Intel™ or theTurion™ family manufactured by AMD™. Processor 210 may constitute asingle core or multiple core processor that executes parallel processessimultaneously. For example, processor 210 may be a single coreprocessor that is configured with virtual processing technologies. Incertain embodiments, processor 210 may use logical processors tosimultaneously execute and control multiple processes. Processor 210 mayimplement virtual machine technologies, or other similar knowntechnologies to provide the ability to execute, control, run,manipulate, store, etc. multiple software processes, applications,programs, etc. One of ordinary skill in the art would understand thatother types of processor arrangements could be implemented that providefor the capabilities disclosed herein.

Service provider system 110 may include one or more storage devicesconfigured to store information used by processor 210 (or othercomponents) to perform certain functions related to the disclosedembodiments. In one example, service provider system 110 may includememory 230 that includes instructions to enable processor 210 to executeone or more applications, such as server applications, networkcommunication processes, and any other type of application or softwareknown to be available on computer systems. Alternatively, theinstructions, application programs, etc. may be stored in an externalstorage or available from a memory over a network. The one or morestorage devices may be a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other type ofstorage device or tangible computer-readable medium.

In one embodiment, service provider system 110 may include memory 230that includes instructions that, when executed by processor 210, performone or more processes consistent with the functionalities disclosedherein. Methods, systems, and articles of manufacture consistent withdisclosed embodiments are not limited to separate programs or computersconfigured to perform dedicated tasks. For example, service providersystem 110 may include memory 230 that may include one or more programs250 to perform one or more functions of the disclosed embodiments.Moreover, processor 210 may execute one or more programs 250 locatedremotely from service provider system 110. For example, service providersystem 110 may access one or more remote programs 250, that, whenexecuted, perform functions related to disclosed embodiments.

Memory 230 may include one or more memory devices that store data andinstructions used to perform one or more features of the disclosedembodiments. Memory 230 may also include any combination of one or moredatabases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft™ SQLdatabases, SharePoint™ databases, Oracle™ databases, Sybase™ databases,or other relational databases. Memory 230 may include softwarecomponents that, when executed by processor 210, perform one or moreprocesses consistent with the disclosed embodiments. In someembodiments, memory 230 may include an image processing database 260 anda neural-network pipeline database 270 for storing related data toenable service provider system 110 to perform one or more of theprocesses and functionalities associated with the disclosed embodiments.

Service provider system 110 may also be communicatively connected to oneor more memory devices (e.g., databases (not shown)) locally or througha network. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by service providersystem 110. By way of example, the remote memory devices may be documentmanagement systems, Microsoft™ SQL database, SharePoint™ databases,Oracle™ databases, Sybase™ databases, or other relational databases.Systems and methods consistent with disclosed embodiments, however, arenot limited to separate databases or even to the use of a database.

Service provider system 110 may also include one or more I/O devices 220that may include one or more interfaces for receiving signals or inputfrom devices and providing signals or output to one or more devices thatallow data to be received and/or transmitted by service provider system110. For example, service provider system 110 may include interfacecomponents, which may provide interfaces to one or more input devices,such as one or more keyboards, mouse devices, touch screens, track pads,trackballs, scroll wheels, digital cameras, microphones, sensors, andthe like, that enable service provider system 110 to receive data fromone or more users (such as via computing device 120).

In example embodiments of the disclosed technology, service providersystem 110 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Theone or more I/O interfaces may be utilized to receive or collect dataand/or user instructions from a wide variety of input devices. Receiveddata may be processed by one or more computer processors as desired invarious implementations of the disclosed technology and/or stored in oneor more memory devices.

While service provider system 110 has been described as one form forimplementing the techniques described herein, those having ordinaryskill in the art will appreciate that other, functionally equivalenttechniques may be employed. For example, as known in the art, some orall of the functionality implemented via executable instructions mayalso be implemented using firmware and/or hardware devices such asapplication specific integrated circuits (ASICs), programmable logicarrays, state machines, etc. Furthermore, other implementations of theterminal 110 may include a greater or lesser number of components thanthose illustrated.

FIG. 3 shows an example embodiment of computing device 120. As shown,computing device 120 may include input/output (“I/O”) device 220 forreceiving data from another device (e.g., service provider system 110),memory 230 containing operating system (“OS”) 240, program 250, and anyother associated component as described above with respect to serviceprovider system 110. Computing device 120 may also have one or moreprocessors 210, a geographic location sensor (“GLS”) 304 for determiningthe geographic location of computing device 120, a display 306 fordisplaying content such as text (e.g., patent claims or legal clauses),text messages, images, and selectable buttons/icons/links, anenvironmental data (“ED”) sensor 308 for obtaining environmental dataincluding audio and/or visual information, and a user interface (“U/I”)device 310 for receiving user input data, such as data representative ofa click, a scroll, a tap, a press, or typing on an input device that candetect tactile inputs. User input data may also be non-tactile inputsthat may be otherwise detected by ED sensor 308. For example, user inputdata may include auditory commands. According to some embodiments, U/Idevice 310 may include some or all of the components described withrespect to input/output device 220 above. In some embodiments,environmental data sensor 308 may include a microphone and/or an imagecapture device, such as a digital camera.

FIG. 4A shows a flowchart of a method 400 for comparing two patentclaims. FIG. 4B shows a flowchart of methods 400A and 400B fordisplaying the results of the comparison of two patent claims. Methods400, 400A, and 400B may be performed by one or more of the serviceprovider system 110 and the computing device 120 of the system 100.

In block 410 of method 400 in FIG. 4A, the system may receive a firstpatent claim having a plurality of first claim elements. Typically, eachclaim element is separated by a semicolon or a comma.

In block 420, the system may generate, using a segmentation algorithm, afirst hidden Markov chain including a plurality of first nodes based onthe first patent claim, the plurality of first nodes each correspondingto one or more of the plurality of first claim elements of the firstpatent claim. The first hidden Markov chain may be a directed graph thatincludes one or more arrows associated with one or more nodes. Forexample, a first first node of the plurality of first nodes may includean arrow connecting to a second first node of the plurality of firstnodes. The arrow may between a first first node to a second first nodemay correspond to a probability of movement from the first first node tothe second first node. In some embodiments, each node of the pluralityof nodes may correspond to an observable state of the hidden Markovchain, and the plain English meaning of each of the plurality of nodesmay correspond to the hidden state of the hidden Markov chain. In someembodiments, the segmentation algorithm may comprise a cascadingchunking algorithm to break the first patent claim into a plurality offirst nodes corresponding to the plurality of first claim elements. Insome embodiments, the segmentation algorithm may implement a hiddenMarkov chain of order n (i.e., n-gram), wherein n can correspond to anyinteger number of letters comprising each of the plurality of firstclaim elements that make up the plurality of first nodes. In someembodiments, the segmentation algorithm may include implementing acascading chunking algorithm. In some embodiments, a dynamic Bayesiannetwork may be used in place of a hidden Markov chain to model theplurality of nodes.

In block 430, the system may summarize the plurality of first nodes. Insome embodiments, summarizing the plurality of first nodes may comprisecalculating the state transitions of the plurality of first nodes.Calculating the state transitions of the plurality of first nodes mayinclude training the system on a data set including a plurality ofpreviously examined patent claims in order to estimate transition stateprobabilities for the first plurality of nodes.

In block 440, the system may receive a second patent claim having aplurality of second claim elements.

In block 450, the system may generate, using the segmentation algorithm,a second hidden Markov chain including a plurality of second nodes basedon the second patent claim. The plurality of second nodes eachcorresponding to one or more of the plurality of second claim elementsof the second patent claim. The second hidden Markov chain be a directedgraph that includes one or more arrows associated with one or morenodes. For example, a first second node of the plurality of second nodesmay include an arrow connecting to a second second node of the pluralityof second nodes. The arrow may between a first second node to a secondsecond node may correspond to a probability of movement from the firstsecond node to the second second node. In some embodiments, each node ofthe plurality of nodes may correspond to an observable state of thehidden Markov chain, and the plain English meaning of each of theplurality of nodes may correspond to the hidden state of the hiddenMarkov chain. In some embodiments, the segmentation algorithm maycomprise a cascading chunking algorithm to break the second patent claiminto a plurality of second nodes corresponding to the plurality ofsecond claim elements. In some embodiments, the segmentation algorithmmay implement a hidden Markov chain of order n, wherein n can correspondto any integer number of letters comprising each of the plurality ofsecond claim elements that make up the plurality of second nodes. Insome embodiments, a dynamic Bayesian network may be used in place of ahidden Markov chain to model the plurality of nodes.

In block 460, the system may summarize the plurality of second nodes. Insome embodiments, summarizing the plurality of second nodes may comprisecalculating the state transitions of the plurality of second nodes.Calculating the state transitions of the plurality of second nodes mayinclude training the system on a data set including a plurality ofpreviously examined patent claims in order to estimate transition stateprobabilities for the second plurality of nodes.

In block 470, the system may compare each of the summarized plurality offirst nodes with each of the summarized plurality of second nodes toidentify a difference in the hidden state (i.e, the plain Englishmeaning) for each of the plurality of first nodes. In an embodiment, thecomparison may include fuzzy matching the summarized plurality of firstnodes with the summarized plurality of second nodes. Fuzzy matching mayhelp identify whether nodes are similar and not necessarily find exactmatches between two nodes of different claim elements. In an embodiment,the comparison may include determining a Levenshtein distance betweenthe summarized plurality of first nodes and the summarized plurality ofsecond nodes. In an embodiment, the comparison may involve the use of adeep learning model or an NN.

In block 480, the system may determine, based on the comparison, whetherthe difference in the hidden Markov state (i.e., the plain Englishmeaning) for each of the plurality of first nodes exceeds apredetermined minimum difference threshold. For example, the system maydetermine whether the Levenshtein distance between two particular nodesexceed a predetermined minimum difference threshold. When the differenceexceeds the predetermined minimum difference threshold, the system movesto block 482 in method 400A of FIG. 4B. When the difference does notexceed the predetermined minimum, the system moves to block 486 inmethod 400B of FIG. 4B.

In block 482 of method 400A, the system may display text of the firstpatent claim in a first color (e.g., green) when the difference exceedsthe predetermined minimum difference threshold. The first color (e.g.,green) may be different from a default text color (e.g., black).

In block 486 of method 400B, the system may display text of the firstpatent claim in a second color (e.g., red) when the difference does notexceed the predetermined minimum difference threshold. The second color(e.g., red) may be different from a first color (e.g., green) and adefault color (e.g., black).

In block 488 of method 400B, the system may, optionally, display awarning that indicates the first and second patent claims are similarwhen the difference does not exceed the predetermined minimum differencethreshold.

FIG. 5A shows a flowchart of a method 500 for comparing two legalclauses. FIG. 5B shows a flowchart of methods 500A and 500B fordisplaying the results of the comparison of two legal clauses. Methods500, 500A, and 500B may be performed by one or more of the serviceprovider system 110 and the computing device 120 of the system 100.

In block 510 of method 500 in FIG. 5A, the system may receive a firstlegal clause. The first legal clause may be from a legal document suchas a contact (e.g., terms of service, assignment, or non-disclosureagreement).

In block 520, the system may generate, using a segmentation algorithm, afirst hidden Markov chain including a plurality of first nodes based onthe first legal clause. Each of the plurality of first nodes correspondto one or more of the plurality of first claim elements of the firstpatent claim. The segmentation of the legal clause into elements may bebased off punctuation. For example, an element may correspond to asentence which is measured by periods. As another example, an elementmay correspond to a phrase based off of commas, semicolons, or periods.The first hidden Markov chain may be a directed graph that includes oneor more arrows associated with one or more nodes. For example, a firstfirst node of the plurality of first nodes may include an arrowconnecting to a second first node of the plurality of first nodes. Thearrow may between a first first node to a second first node maycorrespond to a probability of movement from the first first node to thesecond first node. In some embodiments, each node of the plurality ofnodes may correspond to an observable state of the hidden Markov chain,and the plain English meaning of each of the plurality of nodes maycorrespond to the hidden state of the hidden Markov chain. In someembodiments, the segmentation algorithm may comprise a cascadingchunking algorithm to break the first legal clause into a plurality offirst nodes corresponding to the plurality of first legal clauseelements. In some embodiments, the segmentation algorithm may implementa hidden Markov chain of order n (i.e., n-gram), wherein n cancorrespond to any integer number of letters comprising each of theplurality of first legal clause elements that make up the plurality offirst nodes. In some embodiments, a dynamic Bayesian network may be usedin place of a hidden Markov chain to model the plurality of nodes.

In block 530, the system may summarize the plurality of first nodes. Insome embodiments, summarizing the plurality of first nodes may comprisecalculating the state transitions of the plurality of first nodes.Calculating the state transitions of the plurality of first nodes mayinclude training the system on a data set including a plurality ofpreviously examined legal clauses in order to estimate transition stateprobabilities for the first plurality of nodes.

In block 540, the system may receive a second legal clause.

In block 550, the system may generate, using the segmentation algorithm,a second hidden Markov chain including a plurality of second nodes basedon the second legal clause. Each of the plurality of second nodecorrespond to an element of the second legal clause. Each element may bea sentence of the legal clause. The segmentation of the legal clauseinto elements may be based off punctuation. For example, an element maycorrespond to a sentence which is measured by periods. As anotherexample, an element may correspond to a phrase based off commas orsemicolons. The second hidden Markov chain may be a directed graph thatincludes one or more arrows associated with one or more nodes. Forexample, a first second node of the plurality of second nodes mayinclude an arrow connecting to a second second node of the plurality ofsecond nodes. The arrow may between a first second node to a secondsecond node may correspond to a probability of movement from the firstsecond node to the second second node. In some embodiments, each node ofthe plurality of nodes may correspond to an observable state of thehidden Markov chain, and the plain English meaning of each of theplurality of nodes may correspond to the hidden state of the hiddenMarkov chain. In some embodiments, the segmentation algorithm maycomprise a cascading chunking algorithm to break the second legal clauseinto a plurality of second nodes corresponding to the plurality ofsecond legal clause elements. In some embodiments, the segmentationalgorithm may implement a hidden Markov chain of order n, wherein n cancorrespond to any integer number of letters comprising each of theplurality of second claim elements that make up the plurality of secondnodes. In some embodiments, a dynamic Bayesian network may be used inplace of a hidden Markov chain to model the plurality of nodes.

In block 560, the system may summarize the plurality of second nodes. Insome embodiments, summarizing the plurality of second nodes may comprisecalculating the state transitions of the plurality of second nodes.Calculating the state transitions of the plurality of second nodes mayinclude training the system on a data set including a plurality ofpreviously examined legal clauses in order to estimate transition stateprobabilities for the second plurality of nodes.

In block 570, the system may compare each of the summarized plurality offirst nodes with each of the summarized plurality of second nodes toidentify a difference in the hidden state (i.e., the plain Englishmeaning) for each of the plurality of first nodes. In an embodiment, thecomparison may include fuzzy matching the summarized plurality of firstnodes with the summarized plurality of second nodes. Fuzzy matching mayhelp identify whether nodes are similar and not necessarily find exactmatches between two nodes of different elements of legal clauses. In anembodiment, the comparison may include determining a Levenshteindistance between the summarized plurality of first nodes and thesummarized plurality of second nodes. In an embodiment, the comparisonmay involve the use of a deep learning model or an NN.

In block 580, the system may determine, based on the comparison, whetherthe difference in the hidden Markov state (i.e., the plain Englishmeaning) for each of the plurality of first nodes exceeds apredetermined minimum difference threshold. For example, the system maydetermine whether the determined Levenshtein distance for two particularnodes exceed a predetermined minimum difference threshold. When thedifference exceeds the predetermined minimum difference threshold, thesystem moves to block 582 in method 500A of FIG. 5B. When the differencedoes not exceed the predetermined minimum threshold, the system moves toblock 586 in method 500B of FIG. 5B.

In block 582 of method 500A, the system may display text of the firstlegal clause in a first color (e.g., red) when the difference exceedsthe predetermined minimum difference threshold. The first color (e.g.,red) may be different from a default color (e.g., black) for the text.

In block 586 of method 500B, the system may display text of the firstlegal clause in a second color (e.g., green) when the difference doesnot exceed the predetermined minimum difference threshold. The secondcolor (e.g., green) may be different from a first color (e.g., red) anda default color (e.g., black).

In block 588 of method 500B, the system may, optionally, display awarning that indicates the first and second legal clauses are similarwhen the difference does not exceed the predetermined minimum differencethreshold.

FIG. 6A shows a flowchart of a method 600 for comparing two legalclauses. FIG. 6B shows a flowchart of methods 600A and 600B fordisplaying the results of the comparison of two legal clauses. Methods600, 600A, and 600B may be performed by one or more of the serviceprovider system 110 and the computing device 120 of the system 100.

In block 610, the system may receive a first legal clause. The firstlegal clause may be from a legal document such as a contract (e.g.,terms of service, assignment, or non-disclosure agreement).

In block 620, the system may generate, using a segmentation algorithm, afirst hidden Markov chain including a plurality of first nodes based onthe first legal clause. Each of the plurality of first nodes correspondto one or more of the plurality of first claim elements of the firstpatent claim. The segmentation of the legal clause into elements may bebased off punctuation. For example, an element may correspond to asentence which is measured by periods. As another example, an elementmay correspond to a phrase based off commas or semicolons. The firsthidden Markov chain may be a directed graph that includes one or morearrows associated with one or more nodes. For example, a first firstnode of the plurality of first nodes may include an arrow connecting toa second first node of the plurality of first nodes. The arrow maybetween a first first node to a second first node may correspond to aprobability of movement from the first first node to the second firstnode. In some embodiments, each node of the plurality of nodes maycorrespond to an observable state of the hidden Markov chain, and theplain English meaning of each of the plurality of nodes may correspondto the hidden state of the hidden Markov chain. In some embodiments, thesegmentation algorithm may comprise a cascading chunking algorithm tobreak the first legal clause into a plurality of first nodescorresponding to the plurality of first legal clause elements. In someembodiments, the segmentation algorithm may implement a hidden Markovchain of order n (i.e., n-gram), wherein n can correspond to any integernumber of letters comprising each of the plurality of first legal clauseelements that make up the plurality of first nodes. In some embodiments,a dynamic Bayesian network may be used in place of a hidden Markov chainto model the plurality of nodes. In some embodiments, segmenting theplurality of first nodes may comprise calculating the state transitionsof the plurality of first nodes. Calculating the state transitions ofthe plurality of first nodes may include training the system on a dataset including a plurality of previously examined legal clauses in orderto estimate transition state probabilities for the first plurality ofnodes.

In block 640, the system may receive a second legal clause.

In block 650, the system may generate, using the segmentation algorithm,a second hidden Markov chain including a plurality of second nodes basedon the second legal clause. Each of the plurality of second nodecorrespond to an element of the second legal clause. Each element may bea sentence of the legal clause. The segmentation of the legal clauseinto elements may be based off punctuation. For example, an element maycorrespond to a sentence which is measured by periods. As anotherexample, an element may correspond to a phrase based off commas orsemicolons. The second hidden Markov chain may be a directed graph thatincludes one or more arrows associated with one or more nodes. Forexample, a first second node of the plurality of second nodes mayinclude an arrow connecting to a second second node of the plurality ofsecond nodes. The arrow may between a first second node to a secondsecond node may correspond to a probability of movement from the firstsecond node to the second second node. In some embodiments, each node ofthe plurality of nodes may correspond to an observable state of thehidden Markov chain, and the plain English meaning of each of theplurality of nodes may correspond to the hidden state of the hiddenMarkov chain. In some embodiments, the segmentation algorithm maycomprise a cascading chunking algorithm to break the second legal clauseinto a plurality of second nodes corresponding to the plurality ofsecond legal clause elements. In some embodiments, the segmentationalgorithm may implement a hidden Markov chain of order n, wherein n cancorrespond to any integer number of letters comprising each of theplurality of second claim elements that make up the plurality of secondnodes. In some embodiments, a dynamic Bayesian network may be used inplace of a hidden Markov chain to model the plurality of nodes. In someembodiments, segmenting the plurality of second nodes may comprisecalculating the state transitions of the plurality of second nodes.Calculating the state transitions of the plurality of second nodes mayinclude training the system on a data set including a plurality ofpreviously examined legal clauses in order to estimate transition stateprobabilities for the second plurality of nodes.

In block 670, the system may compare each of the plurality of firstnodes with each of the plurality of second nodes to identify adifference in the hidden state (i.e., the plain English meaning) foreach of the plurality of first nodes. In an embodiment, the comparisonmay include fuzzy matching the plurality of first nodes with theplurality of second nodes. In an embodiment, the comparison may includedetermining a Levenshtein distance between the plurality of first nodesand the plurality of second nodes. In an embodiment, the comparison mayinvolve the use of a deep learning model or an NN.

In block 680, the system may determine, based on the comparison, whetherthe difference in the hidden state (i.e., the plain English meaning) foreach of the plurality of first nodes exceeds a predetermined minimumdifference threshold. For example, the system may determine whether thedetermined Levenshtein distance for two particular nodes exceed apredetermined minimum difference threshold. When the difference exceedsthe predetermined minimum difference threshold, the system moves toblock 682 in method 600A of FIG. 6B. When the difference does not exceedthe predetermined minimum, the system moves to block 686 in method 600Bof FIG. 6B.

In block 682 of method 600A, the system may display text of the firstlegal clause in a first color (e.g., red) when the difference exceedsthe predetermined minimum difference threshold. The first color (e.g.,red) may be different from a default color (e.g., black) for the text.

In block 686 of method 600B, the system may display text of the firstlegal clause in a second color (e.g., green) when the difference doesnot exceed the predetermined minimum difference threshold. The secondcolor (e.g., green) may be different from a first color (e.g., red) anda default color (e.g., black).

In block 688 of method 600B, the system may, optionally, display awarning that indicates the first and second legal clauses are similarwhen the difference does not exceed the predetermined minimum differencethreshold.

FIG. 7 shows a flowchart of a method 700 for comparing two legalclauses. Method 700 may be performed by one or more of the serviceprovider system 110 and the computing device 120 of the system 100.

In block 710, the system may receive a first legal clause. According tosome embodiments, the service provider system 110 receives one or morelegal clauses or an entire legal document. In other embodiments, thelegal clause is received and then recognized as a legal clause ratherthan a non-legal clause (e.g., a clause from a technical report). Insome embodiments, the method may include receiving a document ratherthan receiving a legal clause. The method may further include the stepof identifying a legal clause in the received document. The step ofidentifying may be performed by an RNN using long short-term memory(LS™) units or a CNN.

In block 720, the system normalizes the first legal clause. This mayinclude taking number, dates, acronyms, and abbreviations and convertingthem to full text. For example, “400” would be converted to “fourhundred.”

In block 730, the system translates the first legal clause, using theNN, to a first logical rule set. In some cases, the service providersystem 110 performs the translation. In other cases, the computingdevice 120 performs translation. In some embodiments, the NN is an RNN.In other embodiments, the NN is a CNN. Finally, in some embodiments, theNN is an RCNN.

Regardless of the type of NN used, the NN will be trained. The trainingof the NN includes the use of training data, which includes the use ofpairs of legal clauses with associated logical rule sets. The NN learnsto associate a legal clause to a logical rule set based on receivingnumerous legal clauses and logical rule sets. This type of training isdone prior to the block 710 and creates a workable NN.

In block 740, the system may receive a second legal clause. Receivingthe second legal clause is similar to receiving the first legal clause.

In block 750, the system normalizes the second legal clause. Normalizingthe second legal clause is similar to normalizing the first legalclause.

In block 760, the system translates the second legal clause, using theNN, to a second logical rule set. The translation of the second legalclause may be similar to the translation of the first legal clause.

In block 770, the system compares the first and second logical rulesets. For example, the system may perform fuzzy matching on the firstand second logical rule sets.

In block 780, the system generates, based on the comparison, a summaryof the differences between the first and second legal clauses. Thesystem may generate a redlines version of the first legal clause basedon the second legal clause. In addition, or alternatively, the systemmay generate written explanation of the differences between the firstlegal clause and the second legal clause.

Certain implementations provide the advantage of easily comparing legalclauses. Thus, making comparing legal documents easier.

As used in this application, the terms “component,” “module,” “system,”“server,” “processor,” “memory,” and the like are intended to includeone or more computer-related units, such as but not limited to hardware,firmware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a computing device and thecomputing device can be a component. One or more components can residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets,such as data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal.

Certain embodiments and implementations of the disclosed technology aredescribed above with reference to block and flow diagrams of systems andmethods and/or computer program products according to exampleembodiments or implementations of the disclosed technology. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, may be repeated, or may not necessarily need to be performedat all, according to some embodiments or implementations of thedisclosed technology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks.

As an example, embodiments or implementations of the disclosedtechnology may provide for a computer program product, including acomputer-usable medium having a computer-readable program code orprogram instructions embodied therein, said computer-readable programcode adapted to be executed to implement one or more functions specifiedin the flow diagram block or blocks. Likewise, the computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus to cause a series of operational elements or stepsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide elementsor steps for implementing the functions specified in the flow diagramblock or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specifiedfunctions, and program instruction means for performing the specifiedfunctions. It will also be understood that each block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Certain implementations of the disclosed technology are described abovewith reference to user devices may include mobile computing devices.Those skilled in the art recognize that there are several categories ofmobile devices, generally known as portable computing devices that canrun on batteries but are not usually classified as laptops. For example,mobile devices can include, but are not limited to portable computers,tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearabledevices, and smart phones. Additionally, implementations of thedisclosed technology can be utilized with internet of things (IoT)devices, smart televisions and media devices, appliances, automobiles,toys, and voice command devices, along with peripherals that interfacewith these devices.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described may include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form. By “comprising” or “containing” or “including” is meantthat at least the named element, or method step is present in article ormethod, but does not exclude the presence of other elements or methodsteps, even if the other such elements or method steps have the samefunction as what is named.

As used herein, unless otherwise specified the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

While certain embodiments of this disclosure have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that this disclosure isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangementsincluded within the scope of the appended claims. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

This written description uses examples to disclose certain embodimentsof the technology and also to enable any person skilled in the art topractice certain embodiments of this technology, including making andusing any apparatuses or systems and performing any incorporatedmethods. The patentable scope of certain embodiments of the technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

Example Use Case

The following example use case describes an example of a typical use ofcomparing two patent claims. It is intended solely for explanatorypurposes and not in limitation. In one example, a user receives orgenerates an electronic version of a patent claim on their portablelaptop computer (e.g., computing device 120). Regardless, a user may,via a website on their portable laptop computer (e.g., computing device120), send or upload the first patent claim to the service providersystem 110 for processing. In other words, the service provider system110 received a first patent claim. The service provider system 110 thengenerates a first hidden Markov chain including a plurality of firstnodes based on the first patent claim, the plurality of first nodes eachcorresponding to one or more of the plurality of first claim elements ofthe first patent claim. The service provider system 110 then summarizesthe plurality of first nodes. The service provider system 110 receives asecond patent claim having a plurality of second claim elements. Theservice provider system 110 generates a second hidden Markov chaincomprising a plurality of second nodes based on the second patent claim,the plurality of second nodes each corresponding to one or more of theplurality of second claim elements of the second patent claim. Theservice provider system 110 then summarizes the plurality of secondnodes. The service provider system 110 then compares each of thesummarized plurality of first nodes with each of the summarizedplurality of second nodes to identify a difference for each of theplurality of first nodes. The service provider system 110 thendetermines whether the difference for each of the plurality of firstnodes exceeds a predetermined minimum difference threshold. The serviceprovider system 110 may cause the computing device 120 to display textof the first patent claim in a first color (e.g., green) when thedifference exceeds the predetermined minimum difference threshold withthe first color being different from a default text color (e.g., black).The service provider system 110 may cause the computing device todisplay text of the first patent claim in a second color (e.g., red)when the difference does not exceed the predetermined minimum differencethreshold with the second color being different from both a first color(e.g., green) and a default color (e.g., black). Optionally, the serviceprovider system 110 may cause the computing device 120 to display awarning that indicates the first and second patent claims are similarwhen the difference does not exceed the predetermined minimum differencethreshold.

What is claimed is:
 1. A system, comprising: one or more processors; andmemory in communication with the one or more processors and storinginstructions that, when executed by the one or more processors, areconfigured to cause the system to: receive a first text comprising afirst jargon; normalize the first text; translate, using a neuralnetwork (NN), the normalized first text to a first logical rule set;receive a second text comprising a second jargon; normalize the secondtext; translate, using the NN, the normalized second text to a secondlogical rule set; compare the first logical rule set and the secondlogical rule set; and generate, based on the comparison, a summary ofone or more differences between the first text and the second text. 2.The system of claim 1, wherein the instructions, when executed by theone or more processors, are further configured to cause the system toidentify the first and second texts within a document.
 3. The system ofclaim 1, wherein the instructions, when executed by the one or moreprocessors, are further configured to cause the system to determine,based on the comparison, whether the one or more differences exceed apredetermined minimum difference threshold.
 4. The system of claim 3,wherein the instructions, when executed by the one or more processors,are further configured to cause the system to display the first text ina first color when the one or more differences exceed the predeterminedminimum difference threshold, the first color differing from a defaultcolor.
 5. The system of claim 3, wherein the instructions, when executedby the one or more processors, are further configured to cause thesystem to display the first text in a second color when the one or moredifferences do not exceed the predetermined minimum differencethreshold, the second color differing from both a first color and adefault color.
 6. The system of claim 3, wherein the instructions, whenexecuted by the one or more processors, are further configured to causethe system to display a warning that indicates the first and secondtexts are similar when the one or more differences do not exceed thepredetermined minimum difference threshold.
 7. The system of claim 1,wherein the instructions, when executed by the one or more processors,are further configured to cause the system to compare the first andsecond logical rule sets utilizing fuzzy matching.
 8. A system,comprising: one or more processors; and memory in communication with theone or more processors and storing instructions that, when executed bythe one or more processors, are configured to cause the system to:receive a document comprising a plurality of clauses; identify a firstclause within the plurality of clauses; normalize, using a neuralnetwork (NN), the first clause; translate, using the NN, the normalizedfirst clause to a first logical rule set; identify a second clausewithin the plurality of clauses; normalize, using the NN, the secondclause; translate, using the NN, the normalized second clause to asecond logical rule set; compare the first logical rule set and thesecond logical rule set; and determine, based on the comparison, whetherone or more differences between the first and second clauses exceed apredetermined minimum difference threshold.
 9. The system of claim 8,wherein the instructions, when executed by the one or more processors,are further configured to cause the system to display text of the firstclause in a first color when the one or more differences exceeds thepredetermined minimum difference threshold, the first color differingfrom a default color.
 10. The system of claim 9, wherein theinstructions, when executed by the one or more processors, are furtherconfigured to cause the system to display the text of the first clausein a second color when the one or more differences does not exceed thepredetermined minimum difference threshold, the second color differingfrom both the first color and the default color.
 11. The system of claim9, wherein the instructions, when executed by the one or moreprocessors, are further configured to cause the system to display awarning that indicates the first and second clauses are not similar whenthe difference exceeds the predetermined minimum difference threshold.12. The system of claim 8, wherein the instructions, when executed bythe one or more processors, are further configured to cause the systemto compare the first and second logical rule sets utilizing fuzzymatching.
 13. The system of claim 8, wherein the instructions, whenexecuted by the one or more processors, are further configured to causethe system to identify the first and second clauses utilizing arecurrent neural network (RNN).
 14. The system of claim 8, wherein theinstructions, when executed by the one or more processors, are furtherconfigured to cause the system to identify the first and second clausesutilizing a convolutional neural network (CNN).
 15. A system,comprising: one or more processors; and memory in communication with theone or more processors and storing instructions that, when executed bythe one or more processors, are configured to cause the system to:receive one or more first texts comprising one or more first jargons;normalize, using a first neural network (NN), the one or more firsttexts; translate, using the first NN, the one or more normalized firsttexts to one or more first logical rule sets; receive one or more secondtexts comprising one or more second jargons; normalize, using the firstNN, the one or more second texts; translate, using the first NN, the oneor more normalized second texts to one or more second logical rule sets;compare the one or more first logical rule sets and the one or moresecond logical rule sets; and determine, based on the comparison,whether one or more differences between the one or more first and secondtexts exceed a predetermined minimum difference threshold.
 16. Thesystem of claim 15, wherein the instructions, when executed by the oneor more processors, are further configured to cause the system todisplay the one or more first texts in a first color when the one ormore differences exceed the predetermined minimum difference threshold,the first color differing from a default color.
 17. The system of claim16, wherein the instructions, when executed by the one or moreprocessors, are further configured to cause the system to display theone or more first texts in a second color when the one or moredifferences do not exceed the predetermined minimum differencethreshold, the second color differing from both the first color and thedefault color.
 18. The system of claim 15, wherein the instructions,when executed by the one or more processors, are further configured tocause the system to display a warning that indicates the one or morefirst and second texts are not similar when the one or more differencesexceed the predetermined minimum difference threshold.
 19. The system ofclaim 15, wherein the instructions, when executed by the one or moreprocessors, are further configured to cause the system to identify,using a second NN, the one or more first and second texts within adocument.
 20. The system of claim 19, wherein the second NN is arecurrent neural network (RNN).